Feature extraction using sequential semidefinite programming
Date
2007
Authors
Shen, C.
Li, H.
Brooks, M.
Editors
Bottema, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of DICTA / pp.430-437
Statement of Responsibility
Shen, Chunhua, Li, Hongdong and Brooks, Michael J.
Conference Name
Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (9th : 2007 : Glenelg, Australia)
Abstract
Many feature extraction approaches end up with a trace quotient formulation. Since it is difficult to directly solve the trace quotient problem, conventionally the trace quotient cost is replaced by an approximation such that the generalised eigen-decomposition can be applied. In this work we directly optimise the trace quotient. It is reformulated as a quasi-linear semidefinite optimisation problem, which can be solved globally and efficiently using standard off-the-shelf semidefinite programming solvers. Also this optimisation strategy allows one to enforce additional constraints ( e.g., sparseness constraints) on the projection matrix. Based on this optimisation framework, a novel feature extraction algorithm is designed. Its advantages are demonstrated on several UCI machine learning benchmark datasets, USPS handwritten digits and ORL face data.
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Dissertation Note
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© Copyright 2008 IEEE – All Rights Reserved